In recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Since car accidents may be caused by the driver’s tiredness, there are some assistance systems designed for bringing the attention of a driver. In this paper, we review different measures and discuss its advantages and limitations and we are also discussing different indicators of sleepiness in this paper. We conclude that by designing a hybrid drowsiness detection system that combines the non-intrusive physiological measures with other measures one would accurately determine the drowsiness level of a driver.

 

Published In : IJCSN Journal Volume 3, Issue 1

Date of Publication : 01 February 2014

Pages : 28 - 33

Figures : 01

Tables : 02

Publication Link : IJCSN-2014/3-1/Hybrid-Drowsiness-Detection-System-to-Prevent-Accident-using-Non-Intrusive-Physiological-Measures

 

 

 

Kusuma Kumari B.M : Dept of Computer Science, Tumkur University, University College of Science Tumkur, Karnataka, India

Sunitha K.M : Dept of Computer Science, Bangalore University, Vijaya College Bangalore, Karnataka, India

 

 

 

 

 

 

 

Drowsiness detection

Driver sleepiness

indicators

Hybrid measures

Drowsiness system

In this paper, we have reviewed the various methods available to determine the drowsiness state of a driver. This paper also discusses the various indicators of sleepiness. These were also discussing the advantages and limitations of each measure. It would be worth fusing physiological measures, such as ECG, with behavioral and vehicle-based measures in the development of an efficient drowsiness detection system.

 

 

 

 

 

 

 

 

 

[1] T. Hamada, T. Ito, K. Adachi, T. Nakano, and S. Yamamoto(2003),“Detecting method for Driver’s drowsiness applicable to Individual Features” IEEE proc. Intelligent Transportation Systems, vol.2, pp.1405-1410.

[2] L. Barr, H. Howrach, S. Popkin and R. J. Carroll (2009) “A review and evaluation of emerging driver fatigue detection, measures and technologies”, A Report of US department of transportation, Washington DC, USA.

[3] M. Eriksson and N.P. Papanikolopoulos, (1997), “Eye-tracking for detection of driver fatigue”, IEEE proc. Intelligent Transport System, Boston, MA, pp. 314-319.

[4] A. Eskandarian, and A. Mortazavi (2007), “ Evaluation of a smart algorithm for commercial vehicle driver drowsiness detection”, IEEE Intelligent Vehicles Symposium (IV'07), Istanbul, Turkey, pp. 553-559.

[5] Åkerstedt, T., & Kecklund, G. (2000). Trötthet och trafiksäkerhet - en översikt over kunskapsläget. Stockholm: Institutet för psykosocial medicin och Karolinska institutet.

[6] Paul Stephen Rau (2005), “Drowsy drivers detection and warning system for commercial vehicle drivers: Field proportional test design, analysis, and progress”, Proc. - 19th International Technical Conference on the Enhanced Safety of Vehicles, Washington, D.C.,

[7] Encarta, M. (2004). Retrieved 040217, 2004, from http://encarta.msn.com/encnet/features/dictionary/dictionaryhome.aspx

[8] Sternberg, R. (2001). Cognitive Psychology.

[9] Thorén, B. (1999). Mätning av vakenhetsgrad - Utvärdering av ett befintligt mätsystem. Chalmers Tekniska högskola, Göteborg.

[10] Åkerstedt, T., & Kecklund, G. (2000). Trötthet och trafiksäkerhet - en översikt over kunskapsläget. Stockholm: Institutet för psykosocial medicin och Karolinska institutet.

[11] Belz, S. (2000). An On-Road Investigation of Self-Rating of Alertness and Temporal Separation as Indicators of Driver Fatigue in Commercial Motor Vehicle Operators.Blacksburg, VA: Faculty of the Virginia Polytechnic Institute and State University.

[12] Kircher, A., Uddman, M., & Sandin, J. (2002). Vehicle control and Drowsiness. Linköping: VTI (Swedish National Road and Transport Research Institute).

[13] Thorén, B. (1999). Mätning av vakenhetsgrad - Utvärdering av ett befintligt mätsystem. Chalmers Tekniska högskola, Göteborg.

[14] Galley, N., & Schleicher, R. (2002). Fatigue indicators from the electrooculogram – a research report.

[15] Åkerstedt, T., & Gillberg, M. (1990). Subjective and Objective sleepiness in the active individual. International Journal of Neuroscience, 52, 29-37.

[16] Gillberg, M., Kecklund, G., & Åkerstedt, T. (1994). Relations between performance and subjective ratings of sleepiness during a night awake. Sleep 17(3), 236-241.

[17] Wierwille, W., Ellsworth, L., Wreggit, S., Fairbanks, R., & Kirn, C. (1994). Research on vehicle based driver status/performance monitoring; development, validation and refinement of algorithms for detection of driver drowsiness. (No. DOT HS 808 247): National Highway Safety Administration Final Report.

[18] Galley, N., & Schleicher, R. (2002). Fatigue indicators from the electrooculogram – a research report.

[19] Dinges, D.F. (1995). "An overview of sleepiness and accidents." Journal of sleep research 4: 4–14.

[20] Brown, I.D. (1994). "Driver fatigue." Human factors 36(2): 298–314.

[21] Williamson, A., Feyer, A. and Friswell, R. (1996). "The impact of work practices on fatigue in long distance truck drivers." Accident analysis and prevention 28(6): 709–719.

[22] Dement, W.C. and Carskadon, M.A. (1982). "Current perspectives on daytime sleepiness: the issues." Sleep 5: 56–66.

[23] Desmond, P.A., Matthews, G. and Hancock, P.A. (1997). Dimensions of subjective fatigue states in driving The 14th international conference on alcohol, drugs and traffic safety.

[24] Wright, N., Stone, B., Horberry, T. and Reed, N. (2007). A review of in-vehicle sleepiness detection devices, TRL.

[25] Boverie, S., Giralt, A. and Le Quellec, J.M. (2008). Diagnostic fusion for in vehicle driver vigilance assessment. IFAC Proceedings Volumes (IFAC-PapersOnline).

[26] Liu, C.C., Hosking, S.G. and Lenné, M.G. (2009). "Predicting driver drowsiness using vehicle measures: Recent insights and future challenges." Journal of Safety Research 40(4): 239–245.

[27] Bergasa, L.M., Nuevo, J., Sotelo, M.A., Barea, R. and Lopez, M.E. (2006). "Real-time system for monitoring driver vigilance." IEEE Transactions on Intelligent Transportation Systems 7(1): 63–77.

[28] Sandberg, D. (2008). Analysis and optimization of systems for detecting sleepiness in drivers. Göteborg, Sweden, Chalmers university.

[29] Åkerstedt, T., Ingre, M., Kecklund, G., Anund, A., Sandberg, D., Wahde, M., Philip, P. and Kronberg, P. (2009). "Reaction of sleepiness indicators to partial sleep deprivation, time of day and time on task in a driving simulator - the DROWSI project." Journal of sleep research.

[30] Yang, J.H., Mao, Z.H., Tijerina, L., Pilutti, T., Coughlin, J.F. and Feron, E. (2009). "Detection of driver fatigue caused by sleep deprivation." IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans 39(4): 694–705.

[31] Ingre, M.; ÅKerstedt, T.; Peters, B.; Anund, A.; Kecklund, G. Subjective sleepiness, simulated driving performance and blink duration: Examining individual differences. J. Sleep Res. 2006, 15, 47–53.

[32] Lawrence, B.; Stephen, P.; Howarth, H. An Evaluation of Emerging Driver Fatigue Detection Measures and Technologies; Volpe National Transportation Systems Center Cambridge:Cambridge, UK, 2009.

[33] Lee, B.-G.; Chung, W.-Y. Multi-classifier for highly reliable driver drowsiness detection in Android platform. Biomed. Eng. Appl. Basis Commun. 2012, 24, 147–154.

[34] Sloten, J.; Verdonck, P.; Nyssen, M.; Haueisen, J.; Mizuno, A.; Okumura, H.; Matsumura, M. Development of Neckband Mounted Active Bio-Electrodes for Non-Restraint Lead Method of ECG R Wave. In Proceedings of the 4th European Conference of the International Federation for Medical and Biological Engineering, Antwerp, Belgium, 23–27 November 2008; pp. 1394–1397.

[35] Lee, B.G.; Jung, S.J.; Chung, W.Y. Real-time physiological and vision monitoring of vehicle driver for non-intrusive drowsiness detection. Commun. IET 2011, 5, 2461–2469.

[36] Cheng, B.; Zhang, W.; Lin, Y.; Feng, R.; Zhang, X. Driver drowsiness detection based on multisource information. Hum. Factors Ergon. Manuf. Serv. Indust. 2012, 22, 450–467.